Explore the vast range of titles available.

The escalating burden of diabetes in countries tackling high burden of tuberculosis (TB) has adverse implications for co-infected individuals and National TB control efforts. We aimed to study whether there was a difference in treatment outcome among diabetic and non-diabetic pulmonary TB patients and identify the determinants of treatment outcome among the two groups. This prospective cohort study recruited new patients of pulmonary tuberculosis (PTB) aged 15 years and above who were diagnosed at and registered with Gulab Devi Chest Hospital, Lahore, Pakistan for anti-tuberculosis treatment (ATT). PTB patients were screened for diabetes using random and fasting blood glucose tests. Diabetic and non-diabetic PTB patients were followed up at second, fifth and sixth month of ATT and 6 months after ATT completion to determine treatment outcome. Multivariate logistic regression analysis was conducted to assess association between various factors and treatment outcome. Of 614 PTB patients, (n = 113 [18%]) were diabetic and (n = 501 [82%]) non-diabetic. Final model showed that diabetics were more likely to experience an unfavorable outcome as compared to non-diabetics (adjusted odds ratio [aOR] = 2.70, 95% Confidence Interval [CI] = 1.30 to 5.59). Other predictors of unfavorable outcome included rural residence (aOR = 1.98, 95% CI = 1.14 to 3.47), body mass index less than 18.50 (aOR = 1.89, 95% CI = 1.03 to 3.47) and being a smoker (aOR = 2.03, 95%CI = 1.04 to 3.94). Our study shows unfavorable treatment outcome among diabetic PTB patients. Integrated models of care with screening/testing and management for diabetes and TB could improve TB treatment outcomes.

Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning.

Various communication and behavioral theories have been adopted to address health infodemics. However, there is no framework specially designed for social listening studies using social media data, machine learning, and natural language processing techniques. We aimed to propose a novel yet theory-based conceptual framework for infodemic research. We collected theories and models used in COVID-19 related studies published in peer-reviewed journals, ranging from health behavior, communication, to infodemic studies. These were analyzed and critiqued for their components, and we subsequently proposed a conceptual framework with a demonstration. Accordingly, we proposed our “Social Media Listening for Public Health Behavior” conceptual framework by not only integrating important attributes of existing theories, but also adding new attributes. The proposed conceptual framework can be used to better understand public discourse on social media, and can be integrated with other data analyses to gather a more comprehensive picture.

BackgroundThis study explores changing patterns of healthcare utilisation for chronic diseases during the COVID-19 pandemic in Ontario, Canada. It compares prepandemic and pandemic morbidity and mortality, focusing on physician and emergency department visits, hospitalisations for anxiety, depression and chronic diseases, as well as all-cause mortality rates.MethodsWe constructed a cohort of 2 950 384 adults (18+ years), using administrative health databases, who were living in Ontario, Canada, between the period of January 2017 and March 2023 and recorded the number of visits each individual had in the follow-up period related to chronic conditions. The data were then analysed using an interrupted time-series design to observe changes from before compared with during the pandemic in (1) monthly physician or emergency visits and hospitalisations and (2) monthly all-cause deaths. The exposure in this study was the onset of the COVID-19 pandemic in Ontario, Canada.ResultsIn the prepandemic period, mean monthly PCR-tested visits in Ontario were 364 880, with a steady increase of 1210 visits per month. During the initial phase of the COVID-19 pandemic, there was a decline in physician visits and hospitalisations for chronic diseases. This trend changed, leading to a significant rise in visits that peaked in March 2021, increasing by 1690 visits monthly. From 2022 onwards, visits saw a notable decline, decreasing by 6830 per month (p<0.05), reflecting reduced healthcare utilisation in the later pandemic phases.ConclusionsThe COVID-19 pandemic caused significant fluctuations in healthcare utilisation in Ontario. These changes suggest increased risks of missed diagnoses and delayed care, impacting morbidity and mortality. The results emphasise the importance of adaptable healthcare systems and strong pandemic preparedness to maintain care continuity, especially for chronic disease management, during resource-limited periods.

Background: Despite growing evidence that reduced vegetation cover could be a putative risk factor for mental health disorders, the age- and the sex-specific association between vegetation and mental health disorder cases in urban areas is poorly understood. However, with rapid urbanization across the globe, there is an urgent need to study this association and understand the potential impact of vegetation loss on the mental well-being of urban residents. Objective: This study aims to analyze the spatial association between vegetation cover and the age- and sex-stratified mental health disorder cases in the neighborhoods of Toronto, Canada. Methods: We used remote sensing to detect urban vegetation and Bayesian spatial hierarchical modeling to analyze the relationship between vegetation cover and mental health disorder cases. Specifically, an Enhanced Vegetation Index was used to detect urban vegetation, and Bayesian Poisson lognormal models were implemented to study the association between vegetation and mental health disorder cases of males and females in the 0-19, 20-44, 45-64, and ≥65 years age groups, after controlling for marginalization and unmeasured (latent) spatial and nonspatial covariates at the neighborhood level. Results: The results suggest that even after adjusting for marginalization, there were significant age- and sex-specific effects of vegetation on the prevalence of mental health disorders in Toronto. Mental health disorders were negatively associated with the vegetation cover for males aged 0-19 years (−7.009; 95% CI −13.130 to −0.980) and for both males (−4.544; 95% CI −8.224 to −0.895) and females (−3.513; 95% CI −6.289 to −0.681) aged 20-44 years. However, for older adults in the 45-64 and ≥65 years age groups, only the marginalization covariates were significantly associated with mental health disorder cases. In addition, a substantial influence of the unmeasured (latent) and spatially structured covariates was detected in each model (relative contributions>0.7), suggesting that the variations in area-specific relative risk were mainly spatial in nature. Conclusions: As significant and negative associations between vegetation and mental health disorder cases were found for young males and females, investments in urban greenery can help reduce the future burden of mental health disorders in Canada. The findings highlight the urgent need to understand the age-sex dynamics of the interaction between surrounding vegetation and urban dwellers and its subsequent impact on mental well-being.

Prospective cohort studies are instrumental in generating valid scientific evidence based on identifying temporal associations between cause and effect. Researchers in a developing country like Pakistan seldom undertake cohort studies hence little is known about the challenges encountered while conducting them. We describe the retention rates among tuberculosis patients with and without diabetes, look at factors associated with loss to follow up among the cohort and assess operational factors that contributed to retention of cohort. A prospective cohort study was initiated in October 2013 at the Gulab Devi Chest Hospital, Lahore, Pakistan. We recruited 614 new adult cases of pulmonary tuberculosis, whose diabetic status was ascertained by conducting random and fasting blood glucose tests. The cohort was followed up at the 2nd, 5th and 6th month while on anti-tuberculosis therapy (ATT) and 6months after ATT completion to determine treatment outcomes among the two groups i.e. patients with diabetes and patients without diabetes. The overall retention rate was 81.9% (n=503), with 82.3% (93/113) among patients with diabetes and 81.8% (410/501) among patients without diabetes (p=0.91). Age (p=0.001), area of residence (p=0.029), marital status (p=0.001), educational qualification (p=<0.001) and smoking (p=0.026) were significantly associated with loss to follow up. Respondents were lost to follow up due to inability of research team to contact them as either contact numbers provided were incorrect or switched off (44/111, 39.6%). We were able to retain 81.9% of PTB patients in the diabetes tuberculosis treatment outcome (DITTO) study for 12months. Retention rates among people with and without diabetes were similar. Older age, rural residence, illiteracy and smoking were associated with loss to follow up. The study employed gender matched data collectors, had a 24-h helpline for patients and sent follow up reminders through telephone calls rather than short messaging service, which might have contributed to retention of cohort.

Introduction: Studies of the impact of hepatitis C virus (HCV), hepatitis B virus (HBV) and HIV mono and co-infections on the risk of cancer, particularly extra-hepatic cancer, have been limited and inconsistent in their findings. Methods: In the British Columbia Hepatitis Testers Cohort, we assessed the risk of colorectal, liver, and pancreatic cancers in association with HCV, HBV and HIV infection status. Using Fine and Gray adjusted proportional subdistribution hazards models, we assessed the impact of infection status on each cancer, accounting for competing mortality risk. Cancer occurrence was ascertained from the BC Cancer Registry. Results: Among 658,697 individuals tested for the occurrence of all three infections, 1407 colorectal, 1294 liver, and 489 pancreatic cancers were identified. Compared to uninfected individuals, the risk of colorectal cancer was significantly elevated among those with HCV (Hazard ration [HR] 2.99; 95% confidence interval [CI] 2.55–3.51), HBV (HR 2.47; 95% CI 1.85–3.28), and HIV mono-infection (HR 2.30; 95% CI 1.47–3.59), and HCV/HIV co-infection. The risk of liver cancer was significantly elevated among HCV and HBV mono-infected and all co-infected individuals. The risk of pancreatic cancer was significantly elevated among individuals with HCV (HR 2.79; 95% CI 2.01–3.70) and HIV mono-infection (HR 2.82; 95% CI 1.39–5.71), and HCV/HBV co-infection. Discussion/Conclusion: Compared to uninfected individuals, the risk of colorectal, pancreatic and liver cancers was elevated among those with HCV, HBV and/or HIV infection. These findings highlight the need for targeted cancer prevention and diligent clinical monitoring for hepatic and extrahepatic cancers in infected populations.

This study investigated cases of pregnancy-related listeriosis in British Columbia (BC), Canada, from 2005 to 2014. We described all diagnosed cases in pregnant women (n = 15) and neonates (n = 7), estimated the excess healthcare costs associated with listeriosis, and calculated the fraction of stillbirths attributable to listeriosis, and mask cell sizes 1–5 due to data requirements. Pregnant women had a median gestational age of 31 weeks at listeriosis onset (range: 20–39) and on average delivered at a median of 37 weeks gestation (range: 20–40). Neonates experienced complications but no fatalities. Stillbirths occurred in 1–5 of 15 pregnant women with listeriosis, and very few (0.05–0.24%) of the 2,088 stillbirths in BC in the 10 years were attributed to listeriosis (exact numbers masked). Pregnant women and neonates with listeriosis had significantly more hospital visits, days in the hospital and physician visits than those without listeriosis. Pregnant women with listeriosis had 2.59 times higher mean total healthcare costs during their pregnancy, and neonates with listeriosis had 9.85 times higher mean total healthcare costs during their neonatal period, adjusting for various factors. Despite small case numbers and no reported deaths, these results highlight the substantial additional health service use and costs associated with individual cases of pregnancy-related listeriosis in BC.

Human monkeypox (mpox) virus is a viral zoonosis that belongs to the Orthopoxvirus genus of the Poxviridae family, which presents with similar symptoms as those seen in human smallpox patients. Mpox is an increasing concern globally, with over 80,000 cases in non-endemic countries as of December 2022. In this review, we provide a brief history and ecology of mpox, its basic virology, and the key differences in mpox viral fitness traits before and after 2022. We summarize and critique current knowledge from epidemiological mathematical models, within-host models, and between-host transmission models using the One Health approach, where we distinguish between models that focus on immunity from vaccination, geography, climatic variables, as well as animal models. We report various epidemiological parameters, such as the reproduction number, R 0, in a condensed format to facilitate comparison between studies. We focus on how mathematical modelling studies have led to novel mechanistic insight into mpox transmission and pathogenesis. As mpox is predicted to lead to further infection peaks in many historically non-endemic countries, mathematical modelling studies of mpox can provide rapid actionable insights into viral dynamics to guide public health measures and mitigation strategies.

Objectives To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by glaucoma and their proportion of the total number of vision-impaired individuals. Methods A systematic review and meta-analysis of published population studies and grey literature from 2000 to 2020 was carried out to estimate global and regional trends in number of people with vision loss due to glaucoma. Moderate or severe vision loss (MSVI) was defined as visual acuity of 6/60 or better but <6/18 (moderate) and visual acuity of 3/60 or better but <6/60 (severe vision loss). Blindness was defined as presenting visual acuity <3/60. Results Globally, in 2020, 3.61 million people were blind and nearly 4.14 million were visually impaired by glaucoma. Glaucoma accounted for 8.39% (95% uncertainty intervals [UIs]: 6.54, 10.29) of all blindness and 1.41% (95% UI: 1.10, 1.75) of all MSVI. Regionally, the highest proportion of blindness relating to glaucoma was found in high-income countries (26.12% [95% UI: 20.72, 32.09]), while the region with the highest age-standardized prevalence of glaucoma-related blindness and MSVI was Sub-Saharan Africa. Between 2000 and 2020, global age-standardized prevalence of glaucoma-related blindness among adults >= 50 years decreased by 26.06% among males (95% UI: 25.87, 26.24), and by 21.75% among females (95% UI: 21.54, 21.96), while MSVI due to glaucoma increased by 3.7% among males (95% UI: 3.42, 3.98), and by 7.3% in females (95% UI: 7.01, 7.59). Conclusions Within the last two decades, glaucoma has remained a major cause of blindness globally and regionally.